For many visual tasks, the manner in which the image is represented can have a substantial effect on both the performance and the results of the visual task. Convolutional neural networks (CNN), as known in the art, can learn to produce multiscale representations of an image. The features extracted by the convolutional neural networks are features that are pertinent to the image on which the convolutional network is applied.
An article by Krizhevsky et al., entitled “ImageNet Classification with Deep Convolutional Neural Networks” published in the proceedings from the conference on Neural Information Processing Systems 2012, describes the architecture and operation of a deep convolutional neural network. The CNN of this publication includes eight learned layers (five convolutional layers and three fully-connected layers). The pooling layers in this publication include overlapping tiles covering their respective input in an overlapping manner. The detailed CNN is employed for image classification.
An article by Zeiler et al., entitled “Visualizing and Understanding Convolutional Networks” published on http:/arxiv.org/abs/1311.2901v3, is directed to a visualization technique that gives insight into the function of intermediate feature layers of a CNN. The visualization technique shows a plausible and interpretable input pattern (situated in the original input image space) that gives rise to a given activation in the feature maps. The visualization technique employs a multi-layered de-convolutional network. A de-convolutional network employs the same components as a convolutional network (e.g., filtering and pooling) but in reverse. Thus, this article describes mapping detected features in the produced feature maps to the image space of the input image. In this article, the de-convolutional networks are employed as a probe of an already trained convolutional network.
An article by Simonyan et al., entitled “Deep Inside Convolutional Networks: Visualizing Image Classification Models and Saliency Maps” published on http:/arxiv.org/abs/1312.6034, is directed to visualization of image classification models, learnt using deep Convolutional Networks (ConvNets). This article describes two visualization techniques. The first one generates an image for maximizing the class score based on computing the gradient of the class score with respect to the input image. The second one involves computing a class saliency map, specific to a given image and class.
Reference is now made to US Patent Application Publication Number 2010/0266200 to Atallah et al., and entitled “Image Analysis through Neutral Network Using Image Average Color”. This publication is directed at a computer-implemented image system. The system includes an analysis component and a classification component. The analysis component analyzes image characteristics of an image that includes an average color value. The classification component includes a self-organizing map (e.g., Kohonen neural network) for classifying the image relative to a second image based on classification information computed from the average color value.